scholarly journals What Every Reader Should Know About Studies Using Electronic Health Record Data But Is Afraid To Ask (Preprint)

2020 ◽  
Author(s):  
Isaac S Kohane ◽  
Bruce J Aronow ◽  
Paul Avillach ◽  
Brett K Beaulieu-Jones ◽  
Riccardo Bellazzi ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Real World ◽  
Warning Signs ◽  
Health Record ◽  
Real World Data ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Electronic Health

UNSTRUCTURED Coincident with the tsunami of Covid19-related manuscripts, there has been a surge of studies using Real World Data (RWD), including those obtained from electronic health records. Unfortunately, several of these studies have resulted in withdrawn publication because of concerns regarding their soundness and quality. We argue here that there are pre-analytic hints and warning signs that are useful in judging RWD studies that might otherwise pass statistical muster. We outline several of these signs and suggest that review of RWD manuscripts include those who are familiar with how such data are generated.

scholarly journals From screening to ascertainment of the primary outcome using electronic health records: Challenges in the STRIDE trial

2020 ◽  
Vol 17 (4) ◽  
pp. 346-350
Author(s):  
Denise Esserman
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Care Systems ◽  
Electronic Health ◽  
Data Elements

Electronic health record data are a rich resource and can be utilized to answer a wealth of research questions. It is important when using electronic health record data in clinical trials that systems be put in place and vetted prior to enrollment to ensure data elements can be collected consistently across all health care systems. It is often overlooked how something conceptualized on paper (e.g. use of the electronic health record in a study) can be difficult to implement in practice. This article discusses some of the challenges in using electronic health records in the conduct of the STRIDE (Strategies to Reduce Injuries and Develop Confidence in Elders) trial, how we handled those challenges, and the lessons we learned for the conduct of future trials looking to employ the electronic health record.

scholarly journals Challenges associated with missing data in electronic health records: A case study of a risk prediction model for diabetes using data from Slovenian primary care

2017 ◽  
Vol 25 (3) ◽  
pp. 951-959 ◽  
Author(s):  
Gregor Stiglic ◽  
Primoz Kocbek ◽  
Nino Fijacko ◽  
Aziz Sheikh ◽  
Majda Pajnkihar
Keyword(s):  
Missing Data ◽  
Electronic Health Records ◽  
Prediction Model ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Electronic Health

The increasing availability of data stored in electronic health records brings substantial opportunities for advancing patient care and population health. This is, however, fundamentally dependant on the completeness and quality of data in these electronic health records. We sought to use electronic health record data to populate a risk prediction model for identifying patients with undiagnosed type 2 diabetes mellitus. We, however, found substantial (up to 90%) amounts of missing data in some healthcare centres. Attempts at imputing for these missing data or using reduced dataset by removing incomplete records resulted in a major deterioration in the performance of the prediction model. This case study illustrates the substantial wasted opportunities resulting from incomplete records by simulation of missing and incomplete records in predictive modelling process. Government and professional bodies need to prioritise efforts to address these data shortcomings in order to ensure that electronic health record data are maximally exploited for patient and population benefit.

scholarly journals Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

2020 ◽  
Vol 27 (7) ◽  
pp. 1173-1185 ◽  
Author(s):  
Seyedeh Neelufar Payrovnaziri ◽  
Zhaoyi Chen ◽  
Pablo Rengifo-Moreno ◽  
Tim Miller ◽  
Jiang Bian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Scoping Review ◽  
Real World ◽  
Research Trend ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Explainable Artificial Intelligence ◽  
Record Data ◽  
Electronic Health

Abstract Objective To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions. Materials and Methods We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges. Results Forty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5). Discussion XAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals’ point of view. Conclusion Based on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.

Leveraging electronic health records for predictive modeling of post-surgical complications

2017 ◽  
Vol 27 (11) ◽  
pp. 3271-3285 ◽  
Author(s):  
Grant B Weller ◽  
Jenna Lovely ◽  
David W Larson ◽  
Berton A Earnshaw ◽  
Marianne Huebner
Keyword(s):  
Electronic Health Record ◽  
Predictive Modeling ◽  
Surgical Complications ◽  
Decision Rules ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Electronic Health

Hospital-specific electronic health record systems are used to inform clinical practice about best practices and quality improvements. Many surgical centers have developed deterministic clinical decision rules to discover adverse events (e.g. postoperative complications) using electronic health record data. However, these data provide opportunities to use probabilistic methods for early prediction of adverse health events, which may be more informative than deterministic algorithms. Electronic health record data from a set of 9598 colorectal surgery cases from 2010 to 2014 were used to predict the occurrence of selected complications including surgical site infection, ileus, and bleeding. Consistent with previous studies, we find a high rate of missing values for both covariates and complication information (4–90%). Several machine learning classification methods are trained on an 80% random sample of cases and tested on a remaining holdout set. Predictive performance varies by complication, although an area under the receiver operating characteristic curve as high as 0.86 on testing data was achieved for bleeding complications, and accuracy for all complications compares favorably to existing clinical decision rules. Our results confirm that electronic health records provide opportunities for improved risk prediction of surgical complications; however, consideration of data quality and consistency standards is an important step in predictive modeling with such data.

Automated chronic disease surveillance and visualization using electronic health record data

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Michael Klompas ◽  
Chaim Kirby ◽  
Jason McVetta ◽  
Paul Oppedisano ◽  
John Brownstein ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Record ◽  
Disease Surveillance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health
Sign in / Sign up

Export Citation Format

Share Document